Metrics defined once
ROAS, CAC and contribution, reused everywhere.
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Ad analytics & BI
Ad analytics and BI for teams that report to clients.
Reporting is where data work becomes visible, and where it is judged. We build the analytics layer on top of your warehouse: metrics defined once, dashboards that load, and reporting that assembles itself instead of consuming an analyst's week.
- Automated reporting
- Governed metrics
- Client-ready dashboards
What you get
Reporting that assembles itself.
Reports on a schedule
Cross-platform reporting with no manual step.
Each client sees its own data
Row-level access on client-facing dashboards.
Overspend caught in flight
Pacing and anomaly alerts, not hindsight.
Services
From metrics to measurement.
Performance dashboards
One view across every channel and client, loading in under a second.
Learn morePacing & anomaly detection
Pacing projected to month end and anomaly alerts routed to the account owner.
Learn moreCDP architecture
Identity resolution, unified profiles and consent-aware activation in your warehouse.
Learn moreAgency reporting automation
White-label client reports across Amazon, Walmart, Google, Meta and TikTok, built automatically.
Learn moreMarketing mix modeling
Meridian or Robyn models fed by your own pipeline, to measure what each channel contributes.
Learn moreAttribution data
Your attribution platform in the same warehouse as spend and sales, on one agreed model.
Learn moreThe problem
When reporting is the product, manual does not scale.
For an agency, reporting is not an internal convenience. It is a deliverable the client sees every month, and often the main evidence that the retainer is working. Yet it is usually assembled by hand: exports from six platforms, a spreadsheet with formulas nobody dares touch, and a slide deck built the night before. It scales linearly with headcount, breaks when one person is out, and every disagreement about a number costs a meeting.
What the analytics layer covers
What's included.
- A governed metric layer where ROAS, CAC and contribution are defined once and reused everywhere
- Cross-platform reporting that assembles and delivers on a schedule with no manual step
- Client-facing dashboards with row-level access, so each account sees only its own data
- Attribution and incrementality measurement appropriate to your data, not a default model
- Pacing and anomaly alerting, so overspend is caught in flight rather than in hindsight
- Enablement, so your analysts extend the models themselves
How we work
How we run it.
Step 1
Agree the metric definitions with the people who defend the numbers to clients
Step 2
Model those metrics in dbt with tests, so every surface reads from one place
Step 3
Rebuild the highest-effort recurring report first and measure the hours it returns
Step 4
Layer on dashboards, alerting and deeper measurement once the base is trusted
What we use
Tools, platforms and methods.
- dbt
- Snowflake
- BigQuery
- Looker Studio
- Power BI
- Metabase
- Tableau
FAQ
Common questions.
Do we need a warehouse before this is worth doing?
For anything beyond a couple of platforms, yes. Dashboards wired directly to platform APIs cannot join across sources, cannot hold history once a platform ages data out, and re-query on every page load. The warehouse is what makes the reporting layer both fast and consistent.
Which BI tool should we use?
Usually the one your team already knows. Looker Studio is hard to beat on cost for client-facing marketing reporting. Power BI wins where the organization is already on Microsoft. The tool matters far less than whether the metrics underneath it are modeled and tested, which is where we focus.
How much analyst time does this actually give back?
The pattern we see most often is a multi-day monthly reporting cycle collapsing to a review-and-comment pass. The gain is not only hours: analysts spend them on interpretation instead of assembly, which is what clients are paying for.
Can clients log in and see their own dashboards?
Yes. We implement row-level security so each client sees only their accounts, and we can white-label the surface so it reads as your platform rather than ours.
Go deeper
Guides from our engineers.
Guide
Retail Media Data Engineering: Unifying Amazon, Walmart, and Instacart Reporting
Retail media spend is spread across Amazon, Walmart, Instacart, and more, each with its own dashboard. Here's how to build one data model across all of them.
Read the guideGuide
How to Build a Marketing Data Warehouse with dbt and Snowflake
A practical guide to building a marketing data warehouse with dbt and Snowflake: architecture, modeling layers, metric definitions, and the mistakes that sink these projects.
Read the guideRelated
Related services.
Talk to an engineer.
Book a free 30-minute call with a senior engineer. Tell us which report eats the most hours.